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Data-driven estimates of reservoir properties from 3D/4D seismic A brown field study
Evgeny Tolstukhin*, Reidar Midtun, Pål Navestad, ConocoPhillips Norway Tetyana Kholodna, CapGemini Norway October 13, 2019
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Project: Properties from Seismic Motivation and Business Case
Idea / Innovation: Use Machine Learning to predict reservoir properties directly from 3D seismic and well logs data Motivation: Increase value of G&G data through data-driven approach Build a Machine Learning and AI platform for Subsurface Domain Project scope: Duration: 3 months Prove the concept Evaluate business impact Business impact: Drill less water-wet wells and side-tracks Better understanding of reservoir properties and mechanisms October 13, 2019
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Digitization of Subsurface: seismic and wells data
Survey 2010 Survey 2016 A09 A18 A27 Elastic Impedance Poro well log Swe well log RFT pressure Voxels Table October 13, 2019
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Illustration of resolution and sampling effects
Sample bias Swe 100 ft average Observed Well log vs. Seismic Pressure Swat > 0.5 - Observed - Polynom - Linear - Swe Fly by Pluto with the New Horizons probe | New Scientist October 13, 2019
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Data available: poro, swe, pressure, A09, A18,A27 and ratios
EI A09 EI A09 Poro Swe Swe EI A09 Poro EI A09/A27 October 13, 2019
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Causation vs Correlation: poro, swe, pressure, A09, A18,A27 and ratios
Properties, available only from well logs EI A09 Seismic, available in 3D volume Poro EI A09/A27 October 13, 2019
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Causation vs Correlation
Swe EI A09 Color is Pressure Poro EI A09/A27 October 13, 2019
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Causation vs Correlation
Swe EI A09 Color is Pressure Low Swe High Poro Poro EI A09/A27 October 13, 2019
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Causation vs Correlation
Swe EI A09 High Swe Low Poro Color is Pressure Poro EI A09/A27 October 13, 2019
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Causation vs Correlation: NEXT LEVEL, division into groups or clusters
Pore intervals Color is Formation Scope: Try alternative clustering methods: Density Dbscan K-means Normal Mixture Hierarchical Dimesionality reduction: Multi-Dimensional Scaling Principal Component Analysis Try alternative ML methods: Multi-Adaptive Regression Neural Networks Decision Trees Support Vector Machine Random Forest etc. Pressure Intervals A09 Swat 0-1 October 13, 2019
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Concept illustration: clustering and prediction
SeismicType 1 RockType 1 Cluster 1 Category H Cluster 1 Category H RockType 1 SeismicType 1 RockType 2 Only seismic Use ML model from wells A09, A18, A27 Cluster 2 Category L RockType 2 SeismicType 2 SeismicType 2 Category H Cluster 2 Cluster 2 Category L Category L Cluster 1 RockType 1 Poro, Press, Swe RockType 2 October 13, 2019
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Software architecture
SAS JMP / R-scripts Select Transform Filter Interpolate CARET Predicted Factor: Category Well Master table K-means Clustering 3 Seismic 3 props per formation Random Forest Predict Cluster With Factor: Water and Factor: Category Predict Swe, Poro, Press within Cluster using Random Forest Clusters Software used in the project: • SAS Enterprise Guide 7.1 • JMP • JMP.R version 14.0 Distributions Within Clusters Factor: Category High, Medium, Low Scoring in 3D October 13, 2019
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Agenda Introduction Methodology review Results Validation Summary
October 13, 2019
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Results of new Clustering at well level
Clusters Swe Poro Pressure Seismic A09 October 13, 2019
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Results of new Clustering: zoom
Clusters Swe Poro ed ed ed ed Pressure Seismic A09 October 13, 2019
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BLIND TEST Prediction stage Validation at well level: future wells drilled in 2017-2018
October 13, 2019
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Effect of faults and fault shadows: Well 4 example (prediction validation)
Well 4 water saturation Faulted zone Well 4 water saturation Faulted zone October 13, 2019
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Summary of prediction at well level: wells drilled in 2017-2018
Property Abs. misfit Quality of prediction, percent of correct voxels Poro, unit < 0.03 82% Swe, unit < 0.1 84% Pressure, psi < 1000 psi 81% What data do we have: ML models trained on wells drilled in Reservoir properties scored in 3D using 2016 seismic What data do we compare to: «Blind test» or validation wells drilled in Observed properties from well logs (lumped into «voxels») October 13, 2019
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3D Comparison with simulation model: prediction of 3D Water Saturation, formation average
Polygons are manual interpetations of water fronts based on well and production data October 13, 2019
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Agenda Introduction Methodology review Results Validation Summary
October 13, 2019
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Summary The methodology allowed to predict 3D volumes of porosity, water saturation and pressure: Predictions show good results at wells and in 3D This data-driven model can be further utilized for: Further quantitative analysis Reservoir characterization Multi-disciplinary communication Key learnings from the project: Agile project management Collaboration between data and geoscientists «Test fast, fail fast, adjust fast» Quick feed-in of more data from Subsurface Data Lake: New wells, new seismic, new simulation models, other observations Consider addition of tracers, pressure, temperature and other data Strength of the methodology: Quick to run and update Overall 10 min from training at well level to scoring in 3D October 13, 2019
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Acknowledgements October 13, 2019
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